The leading methods used to address imbalanced data are resampling [1]}, cost-sensitive algorithms [2]}, ensemble methods [3]}, and decoupling feature representations from classification [4]}, [5]}. Resampling generally involves under-sampling majority classes or over-sampling minority classes. These methods attempt to re-balance class weight norms in parametric machine learning models by altering the number of samples per class used during training.
